Deep Learning and Neural Networks with TensorFlow and Keras

Artificial intelligence is transforming how businesses solve complex problems, from predicting market trends to recognizing images. This written course guides you through the foundational concepts of deep learning, enabling you to build powerful models from scratch using modern tools. You will progress from understanding basic artificial neurons to implementing advanced architectures like autoencoders and transfer learning models, gaining the practical skills to apply neural networks to real-world datasets. What you'll learn: - Understand the fundamental architecture of artificial neural networks and deep learning concepts. - Build and train predictive models using the Keras API and TensorFlow. - Apply transfer learning techniques to leverage pre-trained models for computer vision tasks. - Implement autoencoders for dimensionality reduction and data feature extraction. - Develop face recognition systems using established deep learning architectures like FaceNet and VGG-Face. - Practice data preprocessing and model evaluation to ensure robust machine learning performance. The course begins with essential terminology and foundational definitions before moving into practical implementation through detailed written explanations and code snippets. It is designed for beginners interested in data science and AI, requiring no prior machine learning experience. Start your journey into the world of deep neural networks today.